Psychological Factors Influencing Brain-Computer Interface (BCI) Performance

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use.

Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

BackgroundThe motor imagery brain computer interface (MI-BCI) is now available in a commercial product for clinical rehabilitation. However, MI-BCI is still a relatively new technology for commercial rehabilitation application and there is limited prior work on the frequency effect. The MI-BCI has become a commercial product for clinical neurological rehabilitation, such as rehabilitation for upper limb motor dysfunction after stroke. However, the formulation of clinical rehabilitation programs for MI-BCI is lack of scientific and standardized guidance, especially limited prior work on the frequency effect. Therefore, this study aims at clarifying how frequency effects on MI-BCI training for the plasticity of the central nervous system.MethodsSixteen young healthy subjects (aged 22.94 ± 3.86 years) were enrolled in this randomized clinical trial study. Subjects were randomly assigned to a high frequency group (HF group) and low frequency group (LF group). The HF group performed MI-BCI training once per day while the LF group performed once every other day. All subjects performed 10 sessions of MI-BCI training. functional near-infrared spectroscopy (fNIRS) measurement, Wolf Motor Function Test (WMFT) and brain computer interface (BCI) performance were assessed at baseline, mid-assessment (after completion of five BCI training sessions), and post-assessment (after completion of 10 BCI training sessions).ResultsThe results from the two-way ANOVA of beta values indicated that GROUP, TIME, and GROUP × TIME interaction of the right primary sensorimotor cortex had significant main effects [GROUP: F(1,14) = 7.251, P = 0.010; TIME: F(2,13) = 3.317, P = 0.046; GROUP × TIME: F(2,13) = 5.676, P = 0.007]. The degree of activation was affected by training frequency, evaluation time point and interaction. The activation of left primary sensory motor cortex was also affected by group (frequency) (P = 0.003). Moreover, the TIME variable was only significantly different in the HF group, in which the beta value of the mid-assessment was higher than that of both the baseline assessment (P = 0.027) and post-assessment (P = 0.001), respectively. Nevertheless, there was no significant difference in the results of WMFT between HF group and LF group.ConclusionThe major results showed that more cortical activation and better BCI performance were found in the HF group relative to the LF group. Moreover, the within-group results also showed more cortical activation after five sessions of BCI training and better BCI performance after 10 sessions in the HF group, but no similar effects were found in the LF group. This pilot study provided an essential reference for the formulation of clinical programs for MI-BCI training in improvement for upper limb dysfunction.

Motivation moderately influences brain–computer interface (BCI) performance in healthy subjects when monetary reward is used to manipulate extrinsic motivation. However, the motivation of severely paralyzed patients, who are potentially in need for BCI, could mainly be internal and thus, an intrinsic motivator may be more powerful. Also healthy subjects who participate in BCI studies could be internally motivated as they may wish to contribute to research and thus extrinsic motivation by monetary reward would be less important than the content of the study. In this respect, motivation could be defined as “motivation-to-help.” The aim of this study was to investigate, whether subjects with high motivation for helping and who are highly empathic would perform better with a BCI controlled by event-related potentials (P300-BCI). We included N = 20 healthy young participants naïve to BCI and grouped them according to their motivation for participating in a BCI study in a low and highly motivated group. Motivation was further manipulated with interesting or boring presentations about BCI and the possibility to help patients. Motivation for helping did neither influence BCI performance nor the P300 amplitude. Post hoc, subjects were re-grouped according to their ability for perspective taking. We found significantly higher P300 amplitudes on parietal electrodes in participants with a low ability for perspective taking and therefore, lower empathy, as compared to participants with higher empathy. The lack of an effect of motivation on BCI performance contradicts previous findings and thus, requires further investigation. We speculate that subjects with higher empathy who are good perspective takers with regards to patients in potential need of BCI, may be more emotionally involved and therefore, less able to allocate attention on the BCI task at hand.

Brain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control.

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.

Enhancing the Social Impact of Contemporary Music with Neurotechnology

The use of robotic devices to provide active motor support and sensory feedback of ongoing motor intention, by means of a Brain Computer Interface (BCI), has received growing support by recent literature, with particular focus on neurorehabilitation therapies. At the same time, performance in the use of the BCI has become a more critical factor, since it directly influences congruency and consistency of the provided sensory feedback. As motor imagery is the mental simulation of a given movement without depending on residual function, training of patients in the use of motor imagery BCI can be extended beyond each rehabilitation session, and practiced by using simpler devices than rehabilitation robots available in the hospital. In this work, we investigated the use of haptic stimulation provided by vibrating electromagnetic motors to enhance BCI system training. The BCI is based on motor imagery of hand grasping and designed to operate a hand exoskeleton. We investigated whether haptic stimulation at fingerpads proves to be more effective than stimulation at wrist, already experimented in literature, due to the higher density of mechano-receptors. Our results did not show significant differences between the two body locations in BCI performance, yet a wider and more stable event-relateddesynchronization appeared for the finger-located stimulation. Future investigations will put in relation training with haptic feedback at fingerpads with BCI performance using the handexoskeleton, in grasping tasks that naturally involve haptic feedback at fingerpads.

The dynamic clamp technology to implement artificial membrane or synaptic conductances is one of the most successful examples of closed-loop interactions with the nervous system for observation and control purposes (for a review see [1]). We recently proposed a generalization of the dynamic clamp concept to design goal-driven closed-loop interactions with biological systems beyond electrical stimulation and recording in the context of in vitro electrophysiology and animal ethology [2]. Following the same activity-dependent stimulation approach, we designed an assisted closed-loop (ACL) to optimize the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP) [3]. SSVEP based BCIs use multiple visual stimuli such as LEDs or regions on a screen flickering at different frequencies. A subject's brain generates electrical activity at the same fundamental frequency as the visual stimulus on which the subject has focused his/her attention. The ACL consists in the delivery of online information with regard to the control over the given BCI goal both to the human subject and to the system, resulting in an online adaptation of BCI stimuli properties, in this first proof of concept study the combination of the employed flicker frequencies (Figure ​(Figure1).1). The optimization process is realized by first performing a real-time closed-loop search for the best set of stimulation frequencies to achieve the given BCI goal, which informs the system about the most effective flicker frequencies to produce SSVEPs in a given subject. Secondly, a continuous auditory feedback informs the subject about the actual distance to a predefined detection threshold. Figure 1 The assisted closed-loop (ACL) provides online information to the system about the most effective flicker frequencies and to the human subject about the actual distance to the pre-defined SSVEP detection threshold by a continuous auditory feedback (speaker ... Our results show that this shared online information leads to an enhanced BCI efficiency, as compared to classic BCI protocols, by helping them to reach the BCI goal in their interaction. The analysis also shows that the ACL protocol takes into account interindividual variabilities. In particular, baseline resting state EEG measures seem to predict subjects' BCI performances, which indicates that the ACL based BCIs might be expanded to innovative new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research. Taking all together, these results illustrate that assisted closed-loop protocols can reveal dynamics otherwise hidden under traditional stimulation techniques, provide control of regular and pathological states, induce learning processes, bridge between distinct levels of analysis and lead to automation of experiments. The proposed approach might have a large impact for applied uses, such as computer control and biomedical or prosthetic uses, but also in novel paradigms for neuroscientific and biomedical research.

Human-machine systems designed for daily activities must balance direct user control with autonomous system behaviour to reduce cognitive effort while maintaining responsiveness. Douglas et al. have demonstrated how shared-autonomy frameworks can enable BCIs to operate across multiple levels of user involvement, from direct neural control to high-level goal specification. Rather than relying solely on low-level EEG commands, the proposed approach integrates intent inference with adaptive robotic planning, allowing multiple users to coordinate multiple robots in functional daily tasks. A key insight from this contribution is that increasing autonomy can significantly reduce user workload while preserving task accuracy and efficiency, supporting more sustainable long-term BCI use in real-world settings. This work highlights a broader shift in BCI design toward collaborative control paradigms, where machines proactively assist users instead of acting only as passive command receivers.Single-modality EEG BCIs are often limited by noise, signal ambiguity, and performance variability across users and environments. Hybrid approaches that fuse EEG with additional sensing modalities offer a promising solution, as demonstrated by Coutray and their team. Coutray et al. have proven how integrating EEG with eye-tracking improves command disambiguation, interaction speed, and overall control reliability, particularly in immersive virtual reality environments. This hybrid design reduces reliance on high-precision EEG decoding alone and enables more natural, intuitive hands-free interaction, expanding BCI applicability beyond clinical contexts into entertainment, accessibility, and smartenvironment control. This contribution reinforces the growing consensus that multimodal BCIs are more scalable, resilient, and user-friendly than EEG-only systems. Robust BCI performance depends not only on decoding algorithms but also on the user's cognitive and neural state. Mohamed et al. have provided evidence that baseline neural oscillations, particularly precue alpha activity, influence event-related desynchronisation (ERD) strength, a core signal used in motorimagery BCIs. A concrete implication of this finding is that BCIs could dynamically adjust classification thresholds, training protocols, or feedback timing based on real-time cognitive state estimates, potentially improving accuracy, consistency, and user learning rates. These insights support the development of state-aware BCIs that adapt decoding strategies in response to moment-to-moment brain dynamics, improving reliability in naturalistic, everyday environments.Beyond EEG decoding alone, combining physiological stress markers and subjective workload measures enables more comprehensive prediction of user performance in demanding tasks. Wei et al. have demonstrated that interpretable machine-learning models can forecast task outcomes while revealing which physiological factors most strongly influence performance. This is particularly relevant for realworld BCI deployment, where fatigue, stress, and cognitive overload can degrade system reliability. A key insight is that interpretable predictive models can support adaptive intervention strategies, such as adjusting task difficulty or providing rest prompts, while maintaining transparency and user trust.Across the contributions, several overarching themes emerge:1. Multimodal integration: Combining EEG with complementary sensors enhances decoding robustness and interaction flexibility. 2. Adaptive autonomy: Systems that balance user intent with automated assistance reduce workload and improve task efficiency. 3. Machine-learning innovation: Deep and interpretable models improve accuracy while supporting transparency and trust. 4. Cognitive context awareness: Accounting for neural and psychological state enables more reliable and personalised BCI performance.Together, these themes suggest a transition from static, single-user BCIs toward adaptive, context-aware, and multi-agent systems designed for long-term everyday use.

In order to improve information transfer rate, the gaze-independent Brain-computer Interfaces (BCIs) flash several stimuli at same time instead of just flashing one stimulus. Studies had reported that the performance of multi-objective gaze-independent BCIs under covert attention conditions were lower than Matrix speller BCIs under overt attention conditions. Motion visually evoked potentials (mVEPs) was recently introduced as stimuli for visual patterns in BCIs. Combination of motion-onset and color had been explored for a single-objective gaze-independent BCI. But the situation of multi-objective hadn't been discussed. Hence, it is needed to exploring whether or not mVEPs can enhance the performance of multi-objective gaze-independent BCIs. For exploration of the above problem, two spells developed from Geometric Speller (GeoSpell) have been built. Ten healthy participants had participated in this study. Results show that motion-onset cannot improve the performance of multi-objective gaze-independent BCIs in terms of event-related potentials (ERPs), when visual angle doesn't exceed 1 degree.

EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control. We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI, and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI). Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), and statistically outperforms both MI alone (42%) and OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA. Integrating MI and OSA leads to improved performance over both individual methods at the group level and is the best BCI paradigm option for some subjects. This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.

A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Level 1 metrics used to report performance at the output of the BCI Control Module, which translates brain signals into logical control output, and Level 2 metrics at the Selection Enhancement Module, which translates logical control to semantic control. We recommend that: (1) the commensurate metrics Mutual Information or Information Transfer Rate (ITR) be used to report Level 1 BCI performance, as these metrics represent information throughput, which is of interest in BCIs for AAC; 2) the BCI-Utility metric be used to report Level 2 BCI performance, as it is capable of handling all current methods of improving BCI performance; (3) these metrics should be supplemented by information specific to each unique BCI configuration; and (4) studies involving Selection Enhancement Modules should report performance at both Level 1 and Level 2 in the BCI system. Following these recommendations will enable efficient comparison between both BCI Control and Selection Enhancement Modules, accelerating research and development of BCI-based AAC systems.